Bias mitigation. A bias mitigation work related to us is LfF [1]}. LfF used a reweighting-based method to deal with bias in out-of-distribution robustness, and used the cross-entropy loss to calculate the weights. The main differences are as follows. First, LfF focused on biased dataset with spurious correlations, while we focused on long-tailed task with label prior shift.
Second, LfF weights the losses at the sample-level, while xERM weights the losses at the supervision-level. Third, LfF calculates the weights using a biased model and a target debiased model, while xERM calculates the weights using fixed balanced and imbalanced models which can be merged into one.
